the response of marine boundary layer clouds to climate change in a hierarchy of models chris jones...
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The Response of Marine Boundary Layer Clouds to Climate Change in a
Hierarchy of Models
Chris Jones Department of Applied Math
Advisor: Chris BrethertonDepartments of Applied Math and Atmospheric Sciences
VOCALS RF05, 72W, 20S
Overview
• Introduction: Marine boundary layer (MBL) clouds and climate sensitivity
• Idealized local case studies in a hierarchy of models
• The well-mixed MBL from observations• Comparison of model responses to changes in
CO2 and temperature• Summary of proposed future work
Earth’s Radiation Budget:R = Absorbed Solar Radiation – Outgoing Longwave Radiation
(NASA)
Marine boundary layer clouds especially important because…
1. They’re reflective at visible wavelengths
MBL clouds
Earth’s Radiation Budget:R = Absorbed Solar Radiation – Outgoing Longwave Radiation
(images courtesy of Chris Bretherton)
Marine boundary layer clouds especially important because…
1. They’re reflective at visible wavelengths
2. They cover a lot of area
CloudFraction
Cloud forcing = R(clear sky) – R(all sky)
Global net cloud radiative forcing ~ -20 W m-2 (Loeb et al, 2009)
Compared to CO2 ~ 2 W m-2
Earth’s Radiation Budget:R = Absorbed Solar Radiation – Outgoing Longwave Radiation
Marine boundary layer clouds especially important because
1. They’re shiny (reflect incoming solar radiation)
2. They cover a lot of area
3. They’re hard to realistically represent in global climate models• Interplay between
dynamics and physics• Nonlinear• Turbulent• Physics must be
parameterized
Climate Change: Response to radiative forcingR = Absorbed Solar Radiation – Outgoing Longwave Radiation
Δ 𝑅=Δ𝑄+𝜆 Δ𝑇 𝑠: Global mean equilibrium surface temperature change (“sensitivity to ”)
Example: If results in more low cloud, that means more reflected solar radiation, less warming ( is smaller for a given ) and thus a negative cloud feedback
If radiation budget is perturbed by a radiative forcing (e.g., doubling CO2), the Earth’s mean surface temperature adjusts until balance is restored:
Feedback parameter
W m-2 K-1 (Planck)
Cloud contribution most uncertain (next slide)
Cloud feedbacks dominate climate sensitivity uncertainty in GCMs
Clouds dominate overall climate feedback uncertainty
Clouds:- Positive feedback, - Large spread between models
Bony et al. (2006)
Cloud feedbacks dominate climate sensitivity uncertainty in GCMs
Clouds dominate overall climate feedback uncertainty
Clouds:- Positive feedback, - Large spread between models
Bony et al. (2006)
Cloud feedbacks dominate climate sensitivity uncertainty in GCMs
Clouds dominate overall climate feedback uncertainty
Low clouds dominate cloud feedback uncertainty
Soden and Vecchi (2011)
Clouds:- Positive feedback, - Large spread between models
Bony et al. (2006)
Parameterizations of Physical Processes Make Profound Impact
3.2K climate sensitivity 4.0 K climate sensitivity
(Gettelman et al., 2011)
UW turbulence and shallow convection parameterizations largely responsible for increase in climate sensitivity from CAM4 to CAM5 – can our analysis help explain this?
Equilibrium response to 2xCO2
Objectives of This Research• Use a localized, idealized column-oriented analysis of prototypical MBL cloud
regimes to identify and evaluate MBL cloud-climate radiative response mechanisms
• Hierarchy of models:– Large eddy simulation (LES): high resolution cloud resolving model – closest we have to
“observations” in local climate change simulations– Single-column model (SCM): ties results to GCM– Mixed-layer model (MLM): simplified model for interpretive purposes
• Seek to relate SCM back to parent GCM• Scientific Relevance: Understanding mechanisms of change in GCMs is pre-
requisite for constraining through observation and/or improving parameterizations.
• Mathematical Relevance: Investigate impacts of various parts of model formulation (e.g., subgrid parameterizations, model resolution, applied large-scale forcings); to what extent can models be used to interpret the behavior of other models?
Case studies drawn from CGILS Intercomparison
• S12: Shallow Stratocumulus (Sc)• Well-mixed BL
• S11: Transition between Sc and shallow cumulus (Cu)• Onset of BL decoupling• Cu rising into Sc
• S6: Shallow Cu
Zhang et al (2010)
Hierarchy of modelsGCM (CAM5)
SCM (SCAM5)
LES (SAM)
Image courtesy of NOAA
(S6, courtesy of Peter Blossey)
SCAM5 Vertical Resolution
MLM
Primitive equations for liquid static energy () and total water mixing ratio () in this
study
Large-scale advection Subsidence Tendencies due to physical processes, e.g.,• Precipitation• Radiation and clouds• Microphysics• Turbulence
Dynamics
• Moist static energy • Water mixing ratio • Inversion (cloud top)
Δ𝑞𝑡
(Stevens, 2007)
Mixed-layer model equations
Mixed-layer model equations
Advective cooling/drying
Entrainment
surface fluxes Radiation
Precipitation
Δ𝑞𝑡
(Stevens, 2007)
October 2008-November 2008
(http://www.atmos.washington.edu/~robwood/VOCALS/vocals_uw.html)
How reasonable is the well-mixed assumption?
Previous project studied the extent of well-mixed vs. decoupled boundary layers using aircraft data from VOCALS field experiment
• Classified flight legs as well-mixed or decoupled based on gradient of moisture and temperature quantities
Decouplingmetric (s)
Subcloud layer
Cloud layer
𝛿𝑞𝛿𝜃ℓWell-mixed Decoupled
Profile-based decoupling classification: Well-mixed if g kg-1 and KApproximately 30% of region was well-mixed.Well-mixed regions correspond to shallower boundary layers.These conditions are met at S12 location.
Jones et al. (2011)
Case setup and proposed sensitivity studies
Simulation setup• Diurnally averaged
summertime insolation• Models run to steady-state• Large-scale forcings
specified from observations:– Horizontal divergence– Subsidence– Sea surface temperature– Wind profile
CGILS sensitivity studies• Control (CTL)
– Mimics current climate
• 4xCO2 concentration (4xCO2):– Captures “fast” adjustment
• Uniform +2K temp. increase: – Captures temperature-
mediated response– Reduced subsidence (P2K)– Subsidence as in CTL (P2K
OM0)
• All models exhibit similar steady-state mean sensitivities:• 4xCO2 has lower inversion, thinner cloud (positive cloud feedback)• P2K deepens and thickens relative to control (negative cloud feedback)• P2K OM0 thinner than P2K and slightly thinner than CTL (positive cloud
feedback)• Subsidence (large scale dynamics) plays dominant role in P2K response
Preliminary S12 Results: Summary [m] [g m-2] [Wm-2]
SAM (LES) -111 -13 +28SCAM5 (SCM) -176 -12 +54MLM -68 -9 +14
SAM (LES) +109 +2 -2SCAM5 (SCM) +70 +1 -7MLM +114 +32 -30
4xCO2
P2K
SAM (LES) -38 -9 +20
SCAM5 (SCM) +5 -5 +18
MLM -4 -4 +8P2K OM0
MLM 4xCO2 Sensitivity Mechanism:
Increased down-welling LW radiationdecreased cloud top radiative
cooling (~10% decrease) Less turbulence (i.e., less
entrainment)Lower zi
Cloud thickness decreases
CTL
4xCO2
CTL
4xCO2
SCAM5 S12 Resolution SensitivityDefault CAM5 Resolutiondoesn’t sustain a cloud Higher resolution does
Clou
d fr
actio
n
Future Work
– Apply MLM to interpreting other LESs involved in CGILS case study
– Fully investigate SCAM5 S12 behavior• What’s driving the resolution sensitivity?
– Expand analysis to other locations (MLM may not apply)
– Parameter-space representation with SCAM• Use SST, Free troposphere lapse rate, CO2 and/or subsidence
as control parameters
– Find a way to relate the local cloud response in SCAM to the sensitivity in its parent GCM
Future Work (plenty to keep me busy)
– Apply MLM to interpreting other LESs involved in CGILS case study (hypothesis: by tuning entrainment efficiency, can I reproduce their mean properties / sensitivities?)
– Dig into roots of SCAM5 S12 sensitivity (interpret w/MLM when appropriate)• What’s driving the resolution sensitivity?
– Expand analysis to other locations (MLM may not apply)– Parameter-space representation with SCAM, following approach
of Caldwell and Bretherton (2009) MLM study• Use SST, Free troposphere lapse rate, CO2 and/or subsidence as control
parameters
– Find a way to relate the local cloud response in SCAM to the sensitivity in its parent GCM
SAM LES Equations
Khairoutdinov and Randall (2003)
• Prognostic TKE SGS model• Diagnostic cloud water, cloud ice, rain,
and snow• Periodic horizontal domain, surface
fluxes from Monin-Obukhov similarity theory
• ISCCP cloud simulator• Parallel (MPI)
The proposal (remember the proposal? This is a presentation about the proposal …)
• Use MLM to interpret output from other LESs (can “tune” parameterizations and entrainment closure as needed)
• Investigate sensitivities in each model for each location• Map out primitive parameter-space representation
using SCM (like CB09)• Ultimately, most concerned with SCAM, b/c it connects
directly to GCM – to what extent can we use this analysis to shed light on the low cloud-climate mechanisms in CAM5?
Primitive equations for liquid static energy () and total water mixing ratio () in this
study
Large-scale advection Subsidence Tendencies due to physical processes, e.g.,• Precipitation• Radiation and clouds• Microphysics• Surface fluxes• Turbulence
Mixed-layer model equationsPrognostic equations:
Entrainment closure:
• (Moist static energy)• (total water mixing ratio)• : Inversion height
• (vertical turbulent flux of x)• (radiation flux)• (precipitation)
• A: entrainment efficiency
Mixed-layer model equationsPrognostic equations:
Mixed Layer Assumptions:• Vertically uniform profiles below inversion• Surface fluxes from bulk transfer model• Inversion flux given by • No turbulence above inversion• Precipitation parameterized following Wood et al• Radiation from RRTMG radiative transfer model• Subsidence, large scale divergence, SST, surface
pressure, and free troposphere h, q specified at all times
Mixed-layer model equations:
Advection(cooling,drying) Entrainment warming/drying
Latent heat flux Precipitation
Radiative coolingSensible heat fluxsubsidence
Mixed-layer model:
• Well mixed q and h moist thermo variables => vertically uniform.– Bulk aerodynamic formulas for surface flux– Inversion fluxes based on thermo jumps
Advection(cooling,drying) Entrainment warming/drying
Latent heat flux Precipitation
Radiative coolingSensible heat fluxsubsidence
Model run specifics
• Grid resolution– CESM 1.0 (CAM5): 1 deg = 0.9 deg x 1.25 deg x 30
levels– (i.e., ~100 km x 137 km x … [variable])
• Time steps (?)• Length of integration• Numerics / miscellaneous
Outline• Introduction
– Climate sensitivity, feedbacks, and cloud radiative forcing– Why are low clouds important (to climate system, climate sensitivity)?– What has been done, and where does this study fit in?– Feedback flow chart (?)
• Proposal for this study: Localized case studies using a hierarchy of models– CGILS cases– Primitive equations– An assortment of models
• GCM (global models, under-resolved,…)• SCM (single column of the GCM)• LES (high-resolution column model – resolve largest, most energetic eddies, models subgrid)• MLM (idealized reduced order model that uses
– Decoupling work pepper VOCALS throughout
• MLM comparison with LES for S12 (and maybe SCAM?)• Proposed dissertation topic
Outline• Introduction
– What is climate sensitivity and why do we care?– Why are low clouds important (to climate system, climate sensitivity)?– What has been done, and where does this study fit in?– Feedback flow chart (?)
• Proposal for this study– CGILS cases– Primitive equations– An assortment of models
• GCM (global models, under-resolved,…)• SCM (single column of the GCM)• LES (high-resolution column model – resolve largest, most energetic eddies, models subgrid)• MLM (idealized reduced order model that uses
– Decoupling work pepper VOCALS throughout
• MLM comparison with LES for S12 (and maybe SCAM?)• Proposed dissertation topic
Our approach:• Consensus that we need better understanding of the processes
underlying low-cloud response to climate change (i.e., GCM intercomparison studies demonstrate clearly the global average low cloud response is a big uncertainty, but individual models differ in parameterizations of cloud processes, and climate-change output diverges widely between models)
• Use IDEALIZED LOCAL CASE STUDIES (drawn from CGILS intercomparison) to investigate cloud sensitivity in a hierarchy of models (LES, SCM, and MLM) to climate-change inspired tests, with the goals of:– Understanding mechanisms behind cloud sensitivity (i.e., do LES and SCM
agree? Can this behavior be constrained by observations? Is improved parameterization, informed by LES necessary?)
– Connecting these back to the GCM behavior of a given model.
Proposal: use a hierarchy of models to investigate low cloud response to climate perturbations
• Local analysis:– Focus on 3 regions used in CGILS intercomparison
study representing 3 low cloud regimes with idealized large scale forcings
– Use 3 types of column models to investigate cloud sensitivity to a variety of perturbations:
• Ultimate goal: Connect these back to GCM
Subcloud legs (actual cloud base – “well-mixed” cloud base)
drizzle
Profiles Decouplingmetric (s)
Surface layer
Cloud layer
𝛿𝑞𝛿𝜃ℓWell-mixed Decoupled
C-130 flight path (grey)Cloud base (lidar-derived)LCL (“well-mixed cloud base”)
Radar reflectivity(drizzle proxy)
(courtesy of Rob Wood)
We use vertical profiles and subcloud level legs
Inversion Jumps
• Lock (2009) and others have suggested high values of
induce strong entrainment and Sc cloud breakup.
• Strong entrainment might also favor decoupling.
Δ𝑞𝑡 Inversion base
Inversion “top”
Decoupling not correlated with inversion jump parameter
• Use REx C-130 profiles to calculate jumps/decoupling, adjacent subcloud legs to calculate cloud fraction. Restrict to flights before 10:00 LT in left panel.
• κ > 0.4 often (but not always) goes with broken cloud. • For κ < 0.5 there is no obvious correlation of κ and decoupling.• POC and non-POC distributions overlap
Blue = well-mixedRed = decoupledHollow = POCDash = Lock (2009) LES results
CGILS Cases (focus on S12 this talk)
• S12: Shallow Stratocumulus (Sc)• Well-mixed BL => mixed-layer
model appropriate• Focus of remainder of this talk
• S11: Transition between Sc and shallow cumulus (Cu)• Onset of BL decoupling• Cu rising into Sc
• S6: Shallow Cu
Mixed-layer model equations
horizontal advection
Entrainment
surface fluxes Radiation
Precipitation
Δ𝑞𝑡
Marine boundary layer clouds:
1. Reflect incoming solar radiation2. Cover a large fraction of the surface
Clouds in climate models
- change in low cloud amount
for 2CO2
from Stephens (2005)
GFDL
CCM
model number
Decouplingmetric (s)
Subcloud layer
Cloud layer
𝛿𝑞𝛿𝜃ℓWell-mixed Decoupled
Approximately 30% of profiles in VOCALS-REx were well-mixed (blue)
thickness the cloud would have if it was well-mixed
Climate Change: Response to radiative forcingR = Absorbed Solar Radiation – Outgoing Longwave Radiation
Feedback parameter
Δ 𝑅=Δ𝑄+𝜆 Δ𝑇 𝑠
Radiative forcing (e.g., increased CO2)
: Global mean equilibrium surface temperature change
Example: If results in more low cloud, that means more reflected solar radiation, less warming ( is smaller for a given ) and thus a negative cloud feedback
If radiation budget is perturbed by a radiative forcing , the Earth’s mean surface temperature adjusts until balance is restored:
Feedback parameter
W m-2 K-1 (Planck)
Cloud contribution most uncertain
Earth’s Radiation Budget:R = Absorbed Solar Radiation – Outgoing Longwave Radiation
(Infrared satellite image, courtesy of Rob Wood)
Marine boundary layer clouds especially important because…
1. They’re reflective at visible wavelengths
2. They cover a lot of area
Climate Change: Response to radiative forcingR = Absorbed Solar Radiation – Outgoing Longwave Radiation
feedback parameter
Δ 𝑅=Δ𝑄+𝜆 Δ𝑇 𝑠
Radiative forcing (e.g., increased CO2)
Climate sensitivity : Global mean equilibrium surface temperature change due to 2xCO2
Example: If results in more low cloud, that means more reflected solar radiation, less warming ( is smaller for a given ) and thus a negative cloud feedback
Likewise, less low cloud => positive feedback (amplifies warming)
If radiation budget is perturbed by a radiative forcing , the Earth’s mean surface temperature adjusts until balance is restored:
Cloud feedbacks dominate climate sensitivity uncertainty in GCMs
Clouds dominate overall climate feedback uncertainty
Low clouds dominate cloud feedback uncertainty
Clouds:- Positive feedback, - Large spread between models
Bony et al. (2006) Soden and Vecchi (2011)
Earth’s Radiation Budget:R = Absorbed Solar Radiation – Outgoing Longwave Radiation
(NASA)
Marine boundary layer clouds especially important because ...
MBL clouds
The Models• LES (high resolution): System for Atmospheric Model (SAM)
– High resolution cloud resolving model– Largest, most energetic eddies resolved– Subgrid-scale turbulence is modeled– The closest we have to “observations” for climate change simulations– Parallel effort by Peter Blossey and Chris Bretherton for CGILS LES intercomparision
• SCM (single column of global model): SCAM5 (CAM5 GCM, operating in single column mode)– Single grid column from the GCM– Approximately 1 degree horizontal resolution, 30 vertical levels– Parameterize subgrid physical processes
• MLM (idealized, interpretive model):– Idealized reduced order model applicable in Sc region (S12) when MBL remains “well-mixed”– When applicable, good for diagnosing / interpreting sensitivities in other models